Breast cancer is a complex genetic disease driven by the accumulation of multiple molecular alterations. Recent molecular advances in high-throughput genomic, transcriptomic and epigenomic technologies have made it possible to focus on the molecular complexity of breast cancer and help guide cancer prognostication and therapy prediction.
Perou et al. demonstrated that breast cancer can be classified into distinct groups based on their gene expression profiles. The Estrogen Receptor positive (ER+) group is characterized by higher expression of a panel of genes that are typically expressed by breast luminal epithelial cells (‘luminal’ cancer). The ER− branch covered three subgroups of tumors: 1) overexpressing ERBB2 (HER2); 2) expressing genes characteristic of breast basal cells (basal-like cancer); and 3) normal-like samples. The clinical importance is that ER+ tumors typically show good prognosis and basal-like and HER2 tumors have poor prognosis.
Gene expression profiling has also led to the development of two gene-expression assays, Oncotype DX and MammaPrint, which determine the risk of breast cancer recurrence in patients for early stage node-negative breast cancer. Oncotype DX analyzes the expression of 21 genes and calculates a recurrence score to identify the likelihood of cancer recurrence in patients and an assessment of their likely benefit from chemotherapy. MammaPrint analyzes the expression of 70 genes and allows patients (<61 years) with early-stage breast cancer to be categorized as having a high or low risk of distant metastasis. High-risk patients may then be managed with more aggressive therapy.
Many other molecular profiling technologies are used to address similar clinical questions. Representational Oligonucleotide Microarray Analysis (ROMA) detects genomic amplifications and deletions and has enabled detection of certain copy number variation patterns and measures their correlation to patient survival.
Following a cancer diagnosis such as breast cancer and primary treatment of localized cancer, a doctor has many options for therapy. How can the ‘right’ decision for treatment be made? Traditionally, diagnostic imaging has played a critical role in cancer treatment choice by characterizing the location, morphology and spread of the tumor. Cancer is correlated with changes within the DNA and its regulatory potential, and the specific characteristics of the patient's tumor cell molecular profile can direct a clinician to the ‘right’ therapy.
Today, molecular tests categorize patients based on single-gene tests like the aforementioned ER, PR and HER2 gene expression. However, there is still significant variation in treatment response within tumors with similar clinical classification and scope for improved tests using DNA methylation and gene expression. DNA methylation affects gene regulation without change in the genetic code. Abnormal DNA methylation profiles are associated with diseases like cancer. Gene expression profiling assess gene activity at the level of a whole genome.
Several small startups and big companies operate in the area of molecular therapy planning, such as Agendia (MammaPrint™ is a prognostic test) and Genomic Health (Oncotype Dx). The target area of these companies is patient stratification for chemotherapy for subsets of breast cancer patients (such as patients who have lymph node negative, ER positive tumors). In addition there is Adjuvant!, which focuses on providing decision support and therapy planning services using clinical factors such as age, tumor size, node status, grade.
Further refinement in molecular classification however, can result in differing clinical significance. Hence, there is a clinical need for molecular profiling solutions that will provide improved diagnosis, prognosis, response prediction to provide the right chemotherapy, and follow-up to monitor for cancer recurrence.
Hence, an improved medical decision tool or system would be advantageous, and in particular a more efficient and/or reliable system would be advantageous.